Community medical service construction: identifying factors that influence medical choice for patients with non-communicable chronic diseases in the Southwest China.

BACKGROUND: Community medical institutions play a vital role in China's healthcare system. While the number of these institutions has increased in recent years, their construction contents remain insufficient. The potential of community medical institutions in preventing, screening, diagnosing, and treating non-communicable chronic diseases (NCDs) has not been fully utilized. This study aims to assess the status of construction contents in community medical institutions in Southwest China and examine how these contents influence the medical choices of NCD patients. METHODS: Descriptive statistics were used to evaluate the construction content of community medical institutions. Multiple-sets of multinomial logistic regression were employed to analyze the associations and marginal impacts between construction content and medical choices. Shapley value analysis was applied to determine the contribution and ranking of these impacts. RESULTS: Descriptive statistics revealed satisfactory construction contents in community medical institutions. Notably, factors such as service attitude, nursing services, expert consultations, charging standards, medical equipment, medical examinations, privacy protection, and referrals significantly influenced medical choices. Among these, service attitude, charging standards, and privacy protection had the most significant marginal improvement effects on NCD patients' choices, with improvements of 12.7%, 10.2%, and 5.9%, respectively. The combined contribution of privacy protection, medical examinations, service attitude, charging standards, and nursing services to medical choices exceeded 80%. CONCLUSION: Optimizing the service contents of community institutions can encourage NCD patients to seek medical care at grassroots hospitals. This study addresses crucial gaps in existing literature and offers practical insights for implementing new medical reform policies, particularly in underdeveloped regions of Southwest China focusing on hierarchical diagnosis and treatment.

Association between ozone and influenza transmissibility in China.

BACKGROUND: Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS: We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS: Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS: O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Molecular characteristics of clinical IMP-producing Klebsiella pneumoniae isolates: novel IMP-90 and integron In2147.

BACKGROUND: Since the first report of carbapenem-resistant Klebsiella pneumoniae isolates in China in 2007, the prevalence of CRKP and CRE has increased significantly. However, the molecular characteristics of IMP-producing Klebsiella pneumoniae (IMPKp) are rarely reported. METHODS: A total of 29 IMPKp isolates were collected from a Chinese tertiary hospital from 2011 to 2017. Clinical IMPKp were identified by VITEK®MS, and further analyzed by whole-genome DNA sequencing with HiSeq and PacBio RSII sequencer. Sequencing data were analyzed using CSI Phylogeny 1.4, Resfinder, PlasmidFinder and the MLST tool provided by the Centre for Genomic Epidemiology. The analysis results were visualized using iTOL editor v1_1. The open reading frames and pseudogenes were predicted using RAST 2.0 combined with BLASTP/BLASTN searches against the RefSeq database. The databases CARD, ResFinder, ISfinder, and INTEGRALL were performed for annotation of the resistance genes, mobile elements, and other features. The types of blaIMP in clinical isolates were determined by BIGSdb-Pasteur. Integrons were drawn by Snapgene, and the gene organization diagrams were drawn by Inkscape 0.48.1. RESULTS: Four novel ST type, including ST5422, ST5423, ST5426 and ST5427 were identified. The IMP-4 and IMP-1 were the dominant IMP type. The majority of blaIMP-carrying plasmids belonged to IncN and IncHI5. Two novel blaIMP-carrying integrons (In2146 and In2147) were uncovered. A novel variant blaIMP-90 presented in novel integron In2147 has been identified. CONCLUSIONS: IMPKp showed low prevalence in China. Novel molecular characteristics of IMPKp have been identified. Continuous monitoring of IMPKp shall also be carried out in the future.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Multi-channel parallel ultrasound detection based on a photothermal tunable fiber optic sensor array.

A multi-channel parallel ultrasound detection system based on a photothermal tunable fiber optic sensor array is proposed. The resonant wavelength of the ultrasound sensor has a quadratic relationship with the power of a 980-nm heating laser. The maximum tuning range is larger than 15 nm. Through photothermal tuning, the inconsistent operating wavelengths of the Fabry-Perot (FP) sensor array can be solved, and then a multiplexing capacity of up to 53 can be theoretically realized, which could greatly reduce the time required for data acquisition. Then, a fixed wavelength laser with ultra-narrow linewidth is used to interrogate the sensor array. The interrogation system demonstrates a noise equivalent pressure (NEP) as low as 0.12 kPa, which is 5.5-times lower than the commercial hydrophone. Furthermore, a prototype of a four-channel ultrasound detection system is built to demonstrate the parallel detection capability. Compared with the independent detection, the SNR of parallel detection does not deteriorate, proving that the parallel detection system and the sensor array own very low cross talk characteristics. The parallel detection technique paves a way for real-time photoacoustic/ultrasound imaging.

Emissions of BTEXs, NMHC, PAHs, and PCDD/Fs from Co-processing of Oil-based Drilling Cuttings in Brick Kilns.

Oil-based drilling cuttings (OBDC) produced from shale gas development is a hazardous waste that have high calorific values and should be disposed of properly. Burning bricks with OBDC is a promising co-disposal method; however, organic pollutants emitted during this process have not received sufficient attention. In this study, the composition and combustion characteristics of OBDC were determined, and the emissions of typical organic pollutants when burning bricks with the addition of OBDC were investigated; these included benzene series compounds (BTEXs), non-methane total hydrocarbons (NMHC), polycyclic aromatic hydrocarbons (PAHs), and polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). The results showed that OBDC comprised large amounts of alkanes and aromatic hydrocarbons, and combusted mainly in the temperature range of 145-450 °C with an ignition temperature of 145 °C. The co-processing 10% OBDC increased the concentrations of toluene, NMHC, and PAHs in the flue gases by ∼1000%, ∼500%, and 200%, respectively, compared to the control experiment; however, their emission concentrations were within the limits set by the Integrated emission standards of air pollutants of Chongqing. It is worth noting that 26.443 ng/Nm3 PCDD/Fs with a total toxicity of 0.709 ng I-TEQ/Nm3 was generated from the co-processing 10% OBDC, which was ascribed to the high content of chlorine and aromatic hydrocarbons in the OBDC-promoted PCDD/Fs formed during the burning and cooling processes. Though PCDD/Fs in flue gas exceeded the 0.5 ng I-TEQ/Nm3 limit prescribed in the Pollution control standard for hazardous wastes incineration of China, the realistic emission of PCDD/Fs is expected to meet with this emission limit after desulfurization treatment as PCDD/Fs can be absorbed by gypsum. It is recommended that a lower amount of OBDC is added to reduce PCDD/F formation at the source and to take more efficient air pollution control system in order to reach a stricter emission limit of 0.1 ng I-TEQ/Nm3 in EU and USA. Cycling flue gas may also be an effective method to reduce other organic pollutants. Under these conditions, co-processing OBDC in brick kilns can be achieved without serious environmental pollution, making it a potential method for disposal and utilization.

Heavy metal and metalloid emissions during co-processing of waste in a sintering kiln: Migration characteristics in the kiln and long-term leaching from bricks.

Heavy metals (HMs) in mixed hazardous waste can be volatilized in the kiln for preparing sintered bricks, which greatly increases the environmental risk. In this study, the volatilization, transformation, and leaching of HMs from bricks were evaluated. Field tests and laboratory leaching experiments were carried out. HM-contaminated soil was used to prepare sintered bricks at high-temperature in a tunnel kiln. Release of HMs from brick under rainfall conditions was investigated in laboratory simulation experiments. The field tests showed that the total amount of Pb, Zn, Cd distributed to the gas phase were all less than 2%, but the amount of Hg entering the gas phase 40.1%-60.5% in the particulate forms. The As leaching rate increased after sintering of bricks in the kiln, which was attributed to the increased formation of soluble arsenate and the reduced availability of sorption sites. The tank leaching test indicated that the release mechanism of trance elements (Cr, As, Zn, Cd, Pb and Ni) was mainly controlled by diffusion. This study provides useful knowledge for decreasing the volatilization and leaching of HMs from sintered bricks prepared using hazardous waste.

In-situ DNA hybridization detection based on a reflective microfiber probe.

A label-free biosensor based on a reflective microfiber probe for in-situ real-time DNA hybridization detection is proposed and experimentally demonstrated. The microfiber probe is simply fabricated by snapping a non-adiabatic biconical microfiber through closing the oxyhydrogen flame during fiber stretching. Assisted with the Fresnel reflection at the end of microfiber, a reflective microfiber modal interferometer is realized. The in-situ DNA hybridization relies on the surface functionalization of a monolayer of Poly-L-lysine (PLL) and synthetic DNA sequences that bind to a given target with high specificity. The detection processes of DNA hybridization in various concentration of target DNA solutions are monitored in real-time and the experimental results present a minimum detectable concentration of 10pM with good repeatability. Additionally, the detection specificity is also investigated by immersing the microfiber probe into the non-complementary ssDNA solutions and observing the spectral variation. The proposed biosensor has advantages of high sensitivity, compact size, ease of use and simple fabrication, which makes it has great potential to be applied in a lot of fields such as disease diagnosis, medicine, and environmental science.

Epitope-loaded nanoemulsion delivery system with ability of extending antigen release elicits potent Th1 response for intranasal vaccine against Helicobacter pylori.

BACKGROUND: Helicobacter pylori (H. pylori) infection remains a global public health issue, especially in Asia. Due to the emergence of antibiotic-resistant strains and the complexity of H. pylori infection, conventional vaccination is the best way to control the disease. Our previous study found that the N-acetyl-neuroaminyllactose-binding hemagglutinin protein (HpaA) is an effective protective antigen for vaccination against H. pylori infection, and intranasal immunization with the immunodominant HpaA epitope peptide (HpaA 154-171, P22, MEGVLIPAGFIKVTILEP) in conjunction with a CpG adjuvant decreased bacterial colonization in H. pylori-infected mice. However, to confer more robust and effective protection against H. pylori infection, an optimized delivery system is needed to enhance the P22-specific memory T cell response. RESULTS: In this study, an intranasal nanoemulsion (NE) delivery system offering high vaccine efficacy without obvious cytotoxicity was designed and produced. We found that this highly stable system significantly prolonged the nasal residence time and enhanced the cellular uptake of the epitope peptide, which powerfully boosted the specific Th1 responses of the NE-P22 vaccine, thus reducing bacterial colonization without CpG. Furthermore, the protection efficacy was further enhanced by combining the NE-P22 vaccine with CpG. CONCLUSION: This epitope-loaded nanoemulsion delivery system was shown to extend antigen release and elicit potent Th1 response, it is an applicable delivery system for intranasal vaccine against H. pylori.

A novel bivalent fusion vaccine induces broad immunoprotection against Staphylococcus aureus infection in different murine models.

With more and more drug-resistant Staphylococcus aureus strains emerging in hospitals, there is an urgent need to develop an effective vaccine to combat S. aureus infection. In this study, we constructed a novel bivalent fusion vaccine, SpA-DKKAA-FnBPA37-507 (SF), based on the D domain of staphylococcal protein A (SpA) and the A domain of fibronectin-binding protein A (FnBPA). Immunisation with SF induced a more ideal protective effect compared with the single components alone in a sepsis model. It also showed broad immunoprotection against seven FnBPA isotypes. Vaccination with SF induced strong antibodies responses and Th1/Th17 polarized cellular responses. Further we demonstrated the protective effect of antibodies by the opsonophagocytic assay (OPA) and passive immunisation. Moreover, vaccination with SF showed protective efficacy in a murine pneumonia model and skin abscess model. These results suggest that SF can be regarded as a promising vaccine candidate for the prevention of S. aureus infections.

Mechanisms of fibronectin-binding protein A (FnBPA110-263) vaccine efficacy in Staphylococcus aureus sepsis versus skin infection.

Increasing rates of life-threatening infections and decreasing susceptibility to antibiotics urge an effective vaccine targeting Staphylococcus aureus. Here we investigate the role of cellular immunity in FnBPA110-263 mediated protection in Staphylococcus aureus infection. This study revealed FnBPA110-263 broadly protected mice from seven FnBPA isotypes strains in the sepsis model. FnBPA110-263 immunized B-cell deficient mice were protected against lethal challenge, while T-cell deficient mice were not. Reconstituting mice with FnBPA110-263 specific CD4+ T-cells conferred antigen specific protection. In vitro assays indicated that isolated FnBPA110-263 specific splenocytes from immunized mice produced abundant IL-17A. IL-17A deficient mice were not protected from a lethal challenge by FnBPA110-263 vaccination. Moreover, neutralizing IL-17A, but not IFN-γ,reverses FnBPA110-263-induced protective efficacy in sepsis and skin infection model. These findings suggest that IL-17A producing Th17 cells play an essential role in FnBPA110-263 vaccine-mediated defense against S. aureus sepsis and skin infection in mice.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

Enhanced efficacy and anti-biofilm activity of novel nanoemulsions against skin burn wound multi-drug resistant MRSA infections.

Multi-drug resistant MRSA (methicillin-resistant Staphylococcus aureus) is a global problem for human health, especially skin burn wound patients. Therefore, we estimated the antibacterial and anti-biofilm activity of a chlorhexidine acetate nanoemulsion (CNE) by previously ourselves designed against skin burn wound MRSA infections. Compared with its water solution (CHX), CNE showed a better and faster action against MRSA both in vitro and in vivo. Importantly, CNE was more effective at inhibiting biofilm formation and clearing the biofilm. We also found that the cell walls and membranes of MRSA were severely disrupted after treatment with CNE. Moreover, the relative electrical conductivity and the leakage of alkaline phosphates, K(+), Mg(2+), DNA and protein obviously increased because the cell wall and membrane were damaged. These data show that novel CNE is a promising potential antimicrobial candidate, especially for skin burn wound MRSA infections.

[Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest].

Parkinson's disease(PD)diagnosis based on speech data has been proved to be an effective way in recent years.There are still some problems on preprocessing samples,ensemble learning,and so on.The problems can further cause misleading of classifiers,unsatisfactory classification accuracy and stability.This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest.At the end of it,this paper presents a group of experiments carried out with the newest public datasets.Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD.Furthermore,it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects.This paper proposes a new speech diagnosis algorithm for PD based on instance selection;and the method algorithm has a higher and more stable classification accuracy,compared with the other algorithms.

A pro-inflammatory role for Th22 cells in Helicobacter pylori-associated gastritis.

OBJECTIVE: Helper T (Th) cell responses are critical for the pathogenesis of Helicobacter pylori-induced gastritis. Th22 cells represent a newly discovered Th cell subset, but their relevance to H. pylori-induced gastritis is unknown. DESIGN: Flow cytometry, real-time PCR and ELISA analyses were performed to examine cell, protein and transcript levels in gastric samples from patients and mice infected with H. pylori. Gastric tissues from interleukin (IL)-22-deficient and wild-type (control) mice were also examined. Tissue inflammation was determined for pro-inflammatory cell infiltration and pro-inflammatory protein production. Gastric epithelial cells and myeloid-derived suppressor cells (MDSC) were isolated, stimulated and/or cultured for Th22 cell function assays. RESULTS: Th22 cells accumulated in gastric mucosa of both patients and mice infected with H. pylori. Th22 cell polarisation was promoted via the production of IL-23 by dendritic cells (DC) during H. pylori infection, and resulted in increased inflammation within the gastric mucosa. This inflammation was characterised by the CXCR2-dependent influx of MDSCs, whose migration was induced via the IL-22-dependent production of CXCL2 by gastric epithelial cells. Under the influence of IL-22, MDSCs, in turn, produced pro-inflammatory proteins, such as S100A8 and S100A9, and suppressed Th1 cell responses, thereby contributing to the development of H. pylori-associated gastritis. CONCLUSIONS: This study, therefore, identifies a novel regulatory network involving H. pylori, DCs, Th22 cells, gastric epithelial cells and MDSCs, which collectively exert a pro-inflammatory effect within the gastric microenvironment. Efforts to inhibit this Th22-dependent pathway may therefore prove a valuable strategy in the therapy of H. pylori-associated gastritis.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China.

Objective: This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. Methods: Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. Results: With an advance warning set at three days, the SEAR model outperformed five primary models - logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model - in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. Conclusion: The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

Identification of suitable habitats and priority conservation areas under climate change scenarios for the Chinese alligator (Alligator sinensis).

Amphibians and reptiles, especially the critically endangered Chinese alligators, are vulnerable to climate change. Historically, the decline in suitable habitats and fragmentation has restricted the distribution of Chinese alligators to a small area in southeast Anhui Province in China. However, the effects of climate change on range-restricted Chinese alligator habitats are largely unknown. We aimed to predict current and future (2050s and 2070s) Chinese alligator distribution and identify priority conservation areas under climate change. We employed species distribution models, barycenter migration analyses, and the Marxian model to assess current and future Chinese alligator distribution and identify priority conservation areas under climate change. The results showed that the lowest temperature and rainfall seasonality in the coldest month were the two most important factors affecting the distribution of Chinese alligators. Future predictions indicate a reduction (3.39%-98.41%) in suitable habitats and a westward shift in their distribution. Further, the study emphasizes that suitable habitats for Chinese alligators are threatened by climate change. Despite the impact of the Anhui Chinese Alligator National Nature Reserve, protection gaps persist, with 78.27% of the area lacking priority protected area. Our study provides crucial data for Chinese alligator adaptation to climate change and underscores the need for improved conservation strategies. Future research should refine conservation efforts, consider individual plasticity, and address identified limitations to enhance the resilience of Chinese alligator populations in the face of ongoing climate change.